Awesome Nlp

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

💫 Industrial-strength Natural Language Processing (NLP) in Python

🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools

Oxford Deep NLP 2017 course

A very simple framework for state-of-the-art Natural Language Processing (NLP)

Open source annotation tool for machine learning practitioners.

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Leveraging BERT and c-TF-IDF to create easily interpretable topics.

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

Language Technology Platform

Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets

Snips Python library to extract meaning from text

A Code-First Introduction to NLP course

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)

MITIE: library and tools for information extraction

NLP made easy

🤗 Evaluate: A library for easily evaluating machine learning models and datasets.

Machine Learning University: Accelerated Natural Language Processing Class

Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

Beautiful visualizations of how language differs among document types.

Basic Utilities for PyTorch Natural Language Processing (NLP)

BERT score for text generation

Sequence-to-sequence framework with a focus on Neural Machine Translation based on PyTorch

Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing

A Neural Framework for MT Evaluation

Fast topic modeling platform

Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx

PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).

A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI :octocat:

An NLP library for the Urdu language. It comes with a lot of battery included features to help you process Urdu data in the easiest way possible.

The tool to make NLP datasets ready to use

BLLIP reranking parser (also known as Charniak-Johnson parser, Charniak parser, Brown reranking parser) See http://pypi.python.org/pypi/bllipparser/ for Python module.

DaNLP is a repository for Natural Language Processing resources for the Danish Language.

Comprehensive NLP Evaluation System

Sentiment Classification using Word Sense Disambiguation

A Multilingual Latent Dirichlet Allocation (LDA) Pipeline with Stop Words Removal, n-gram features, and Inverse Stemming, in Python.

A keyphrase extractor for Persian

Collection of Urdu datasets for POS, NER, Sentiment, Summarization and NLP tasks.